Coal mine personnel tracking is of great significance for realizing intelligent perception of personnel safety situation, as well as improving the level of accurate monitoring and intelligent safety management of mine personnel. Based on the unique advantage of infrared video images in highlighting the visual saliency of personnel targets in coal mine, this paper improves the personnel trackingin infrared monitoring videos of mines by improving the ARCF tracker. In order to identify abnormal tracking of personnel targets in video images, first, the average value of the tracking response map obtained by the ARCF model is calculated, and the parts of the response map that are less than the average value are filtered out. Secondly, the 8-neighborhood comparison method is used to search for the maximum peak point of the response map, and the similarity error between it and the highest peak point of the response map is calculated. Finally, by calculating the rate of change in the similarity error between the peak points of the response map in adjacent two frames of video, it is determined whether the tracking result of personnel target in the current frame of video is abnormal. Experiment results show that compared with the trackers of KCF, BIT, DSST, ARCF and IARCF, the proposed method shows better performance in infrared video tracking of mine personnel.
The High Resolution Range Profile (HRRP) contains rich target information and is commonly utilized in the field of radar automatic target recognition. Most of the key targets of concern in practical applications are non-cooperative, making it challenging to acquire data for such targets. This often results in an imbalance in target categories within datasets, thereby impacting the classification performance of models. To address these issues, this paper proposes the Weighted-SMOTE (Synthetic Minority Over-sampling Technique) algorithm. The algorithm assigns different weights to the number of synthetic new samples for each sample based on the Euclidean distance between minority class samples and the remaining samples. Furthermore, it utilizes the MCNN-TEAN (Multi-scale Convolutional Neural Network – TransEncoder Attention Network) model to extract multi-level features and capture long-term dependencies between feature dimensions, thereby improving the classification accuracy of targets.
Various non-coal foreign objects in raw coal seriously affect the safety and efficiency of coal production. As an attractive object recognition method, object recognition based on computer vision has achieved fruitful results while the accuracy and speed are still great challenges because of the poor image quality results of high conveyor belt speeding and uneven illumination. In this paper, a non-coal foreign object recognition method based on deep learning was proposed. Firstly, based on the noise reduction, image enhancement and image segmentation, a combinatorial optimization method is proposed to preprocess the low-quality images. Secondly, from three aspects including network structure, activation function and decoupling prediction, an improved YOLOv5 algorithm is proposed. Finally, by fusing the visual attention mechanism, the feature extraction quality of the YOLOv5 model is further optimized to enrich the semantic information of foreign objects in the image. The experimental results show that, the proposed method can obtain 88.14% recognition accuracy for gangue objects and 87.10% recognition accuracy for iron objects. Meanwhile, we have achieved as high as 27fps recognition speed, which can meet the real-time recognition on high-speed coal belt conveyor. CCS CONCEPTS • Computing methodologies →Computer graphics →Image manipulation →Image processing; • Computing methodologies →Artificial intelligence → Computer vision; • Computer systems organization →Real-time systems; • Computing methodologies → Machine learning.
The purpose of cross-corpus speech emotion recognition is to transfer the capability of recognizing emotions from speech, learned in a richly annotated corpus, to an unannotated corpus. Due to significant differences between various corpora, this task is highly challenging. Current methods often predict the emotions of unseen samples by learning the correlation between inputs and labels, but their generalization performance is not ideal. Therefore, this paper proposes a cross-corpus speech emotion recognition method based on causal feature disentanglement (CFDM). Specifically, we designed a causal feature disentanglement framework that includes two feature encoders, ingeniously crafting loss functions to learn causal features and confounding features separately. Then, a feature self-enhancement method was developed by disordering and summing the confounding features to further strengthen the disentanglement of causal and confounding features. This framework is versatile and can be adapted to existing emotion recognition models to extract causal features for emotion recognition. Additionally, extended experiments conducted on five widely used speech corpora confirmed the effectiveness of the proposed method.
With the rapid development of deep learning technology, ship target detection plays a crucial role in maritime safety. This paper proposes a ship image fusion method based on convolutional neural networks to enhance detection accuracy and robustness, which provides clear visible visual images during night-time navigation. Firstly, the SCAF (Spatial and Channel Attention Feature Exchange) attention mechanism is incorporated after the first three layers of convolution in the feature extraction module to obtain the spatial salient information and perform the complementary operations on two modal features. Secondly, a light perception network is introduced to calculate the probability of the current scene being daytime or night-time, and this probability is used as the weight of the light loss function in the fusion network to guide the training of the fusion network. Finally, texture detail loss and auxiliary intensity loss are introduced to retain the texture and intensity information of the ship. Experimental results show the fusion effects between proposed model and compared baselines by six evaluation metrics, including Mutual Information (MI), Spatial Frequency (SF), Visual Information Fidelity (VIF), Structural Similarity (SSIM), Fusion Quality (Qabf), and Average Gradient (AG). The compared results prove that the proposed method improves the average performance by 10.4%, 50.9%, 10.8%, 37.6%, 4.0%, and 21.9% respectively compared to the average performance of baseline models.
